vectorize 1d interpolators

review changes

xarray/core/missing.py

@@ -138,6 +138,7 @@ def __init__(
         copy=False,
         bounds_error=False,
         order=None,
+        axis=-1,
         **kwargs,
     ):
         from scipy.interpolate import interp1d
@@ -173,6 +174,7 @@ def __init__(
             bounds_error=bounds_error,
             assume_sorted=assume_sorted,
             copy=copy,
+            axis=axis,
             **self.cons_kwargs,
         )
 
@@ -496,10 +498,13 @@ def _get_interpolator(
             kwargs.update(method=method)
             interp_class = ScipyInterpolator
         elif method == "barycentric":
+            kwargs.update(axis=-1)
             interp_class = _import_interpolant("BarycentricInterpolator", method)
         elif method in ["krogh", "krog"]:
+            kwargs.update(axis=-1)
             interp_class = _import_interpolant("KroghInterpolator", method)
         elif method == "pchip":
+            kwargs.update(axis=-1)
             interp_class = _import_interpolant("PchipInterpolator", method)
         elif method == "spline":
             # utils.emit_user_level_warning(
@@ -512,6 +517,10 @@ def _get_interpolator(
             kwargs.update(method=method)
             interp_class = SplineInterpolator
         elif method == "akima":
+            kwargs.update(axis=-1)
+            interp_class = _import_interpolant("Akima1DInterpolator", method)
+        elif method == "makima":
+            kwargs.update(method="makima", axis=-1)
             interp_class = _import_interpolant("Akima1DInterpolator", method)
         elif method == "makima":
             kwargs.update(method="makima")
@@ -761,8 +770,7 @@ def interp_func(var, x, new_x, method: InterpOptions, kwargs):
 
 def _interp1d(var, x, new_x, func, kwargs):
     # x, new_x are tuples of size 1.
-    x, new_x = x[0].data, new_x[0].data
-
+    x, new_x = x[0], new_x[0]
     rslt = func(x, var, **kwargs)(np.ravel(new_x))
     if new_x.ndim > 1:
         return reshape(rslt, (var.shape[:-1] + new_x.shape))

xarray/tests/test_interp.py

@@ -133,6 +133,7 @@ def func(obj, new_x):
     assert_allclose(actual, expected)
 
 
+@requires_scipy
 @pytest.mark.parametrize(
     "use_dask, method",
     (
@@ -152,37 +153,38 @@ def func(obj, new_x):
     ),
 )
 def test_interpolate_vectorize(use_dask: bool, method: str) -> None:
-    if not has_scipy:
-        pytest.skip("scipy is not installed.")
-
     # scipy interpolation for the reference
     def func(obj, dim, new_x, method):
         scipy_kwargs = {}
         interpolant_options = {
-            "barycentric": "BarycentricInterpolator",
-            "krogh": "KroghInterpolator",
-            "pchip": "PchipInterpolator",
-            "akima": "Akima1DInterpolator",
-            "makima": "Akima1DInterpolator",
+            "barycentric": scipy.interpolate.BarycentricInterpolator,
+            "krogh": scipy.interpolate.KroghInterpolator,
+            "pchip": scipy.interpolate.PchipInterpolator,
+            "akima": scipy.interpolate.Akima1DInterpolator,
+            "makima": scipy.interpolate.Akima1DInterpolator,
         }
 
         shape = [s for i, s in enumerate(obj.shape) if i != obj.get_axis_num(dim)]
         for s in new_x.shape[::-1]:
             shape.insert(obj.get_axis_num(dim), s)
 
         if method in interpolant_options:
-            interpolant = getattr(scipy.interpolate, interpolant_options[method])
+            interpolant = interpolant_options[method]
             if method == "makima":
                 scipy_kwargs["method"] = method
             return interpolant(
                 da[dim], obj.data, axis=obj.get_axis_num(dim), **scipy_kwargs
             )(new_x).reshape(shape)
         else:
-            scipy_kwargs["kind"] = method
-            scipy_kwargs["bounds_error"] = False
-            scipy_kwargs["fill_value"] = np.nan
+
             return scipy.interpolate.interp1d(
-                da[dim], obj.data, axis=obj.get_axis_num(dim), **scipy_kwargs
+                da[dim],
+                obj.data,
+                axis=obj.get_axis_num(dim),
+                kind=method,
+                bounds_error=False,
+                fill_value=np.nan,
+                **scipy_kwargs,
             )(new_x).reshape(shape)
 
     da = get_example_data(0)